Network provisioning processes enable a network to determine the location and size of different network resources available for long-term deployment, typically in the order of several months or years. Such provisioning exercises are typically based solely on projected traffic demands within a network and the agreed traffic exchanges across other networks.
Traditionally, network provisioning has been based on underlying traffic-characterization models, traffic-flow models and performance models. The traditional provisioning process worked well in the telephone network where traffic loads were predictable. The traffic could be characterized with a reasonable accuracy, and the respective performance models that relate the performance to the traffic loads and the network resources were both accurate and tractable.
However, none of these conditions applies to fast-evolving data networks. Accurate traffic characterization at both the microscopic and macroscopic levels is not feasible in the rapidly evolving data networks. Microscopic characterization refers to a parametric description of each traffic stream, whereas macroscopic characterization is concerned with the spatial distribution of traffic. Microscopic-level characterization is difficult due to the rapidly changing nature of the traffic; macroscopic-level characterization is difficult due to changing network services.
Elaborate traffic characterization models and their ensuing provisioning models, even if believed to be accurate, maybe applicable over only relatively short timescales. It would take researchers years to develop and refine such models only to discover, before the exercise is complete, that the models are obsolete. A good example is the development of the tedious Markov Modulated Poisson Process, which is based on assumptions that have since been deemed to be inadequate. Another extreme is the self-similar-traffic model, which would yield an unduly pessimistic estimation of the network performance. In addition to the inadequacy of these models, the traffic parameters required to implement the mathematical models are difficult to determine. Data traffic is a rapidly moving target and its composition changes with technology. Higher access capacity, new applications, new protocols, etc., have a significant effect on the traffic characteristics at both the microscopic and macroscopic levels.
The use of precise microscopic models, even if they were attainable and tractable, cannot be justified, given the rapid change of the traffic nature. Instead, a simplified traffic model may possibly be used as long as the values of the characterizing parameters are updated frequently. A simplified traffic model also leads to a simplified microscopic provisioning model that facilitates the computation of traffic-flow across the network. The use of frequently updated simplified models, however, requires traffic monitoring, at least at each source node, and frequent execution of provisioning models. The network performance, however defined, must still be measured in order to validate the provisioning model. Furthermore, considering the unavailability of a satisfactory traffic model, it is difficult to determine what to measure and how to extract parameters from the measurements. Moreover, this solution would require the use of both traffic models and traffic monitoring, which would require excessive computing time, after which relevant, current data on which to base provisioning decisions may not be easily obtained.
Another issue to be considered is that most network configurations today are carried out statically through manual provisioning intervention. Core nodes provided in such networks are cross-connectors, optical and/or electronic, which are configured on a semi-permanent basis. Some proposals like the ITU-T's Automatically Switched Transport Network (ASTN) introduce dynamic configurations, but still require the configuration requirements to be determined through manual intervention. Therefore there is still a need to automatically predict such configuration requirements through learning based on measurements in the network.